{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3FPxerMlRNom",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "executionInfo": {
     "elapsed": 3834,
     "status": "ok",
     "timestamp": 1652090313415,
     "user": {
      "displayName": "Jayden Baxter",
      "userId": "11604221609513949563"
     },
     "user_tz": -480
    },
    "id": "3FPxerMlRNom",
    "outputId": "0ac96a9d-c08d-4603-b70c-579d3a1644b6"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "tf.test.gpu_device_name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fFtXrXlDRaxR",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 596,
     "status": "ok",
     "timestamp": 1644746688026,
     "user": {
      "displayName": "Jayden Baxter",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "11604221609513949563"
     },
     "user_tz": -480
    },
    "id": "fFtXrXlDRaxR",
    "outputId": "cd8ffcc8-45b5-426a-adf0-577e612399be"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sun Feb 13 10:04:46 2022       \n",
      "+-----------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
      "|-------------------------------+----------------------+----------------------+\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
      "|                               |                      |               MIG M. |\n",
      "|===============================+======================+======================|\n",
      "|   0  Tesla K80           Off  | 00000000:00:04.0 Off |                    0 |\n",
      "| N/A   75C    P0    78W / 149W |    145MiB / 11441MiB |      0%      Default |\n",
      "|                               |                      |                  N/A |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "                                                                               \n",
      "+-----------------------------------------------------------------------------+\n",
      "| Processes:                                                                  |\n",
      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
      "|        ID   ID                                                   Usage      |\n",
      "|=============================================================================|\n",
      "+-----------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "# 查看显存情况\n",
    "!/opt/bin/nvidia-smi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "KXsW92kPmvYY",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 21859,
     "status": "ok",
     "timestamp": 1652117838210,
     "user": {
      "displayName": "Jayden Baxter",
      "userId": "11604221609513949563"
     },
     "user_tz": -480
    },
    "id": "KXsW92kPmvYY",
    "outputId": "66151382-64ca-42be-8fe6-240112ad61a4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/gdrive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/gdrive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e8f0681e-e017-4583-a6c5-dca4c3719142",
   "metadata": {
    "executionInfo": {
     "elapsed": 3313,
     "status": "ok",
     "timestamp": 1652117844622,
     "user": {
      "displayName": "Jayden Baxter",
      "userId": "11604221609513949563"
     },
     "user_tz": -480
    },
    "id": "e8f0681e-e017-4583-a6c5-dca4c3719142"
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras import datasets,layers,models,optimizers\n",
    "from tensorflow import keras\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "55f3df04-b7ee-4ad8-b76c-11f497287156",
   "metadata": {
    "executionInfo": {
     "elapsed": 876,
     "status": "ok",
     "timestamp": 1652117846652,
     "user": {
      "displayName": "Jayden Baxter",
      "userId": "11604221609513949563"
     },
     "user_tz": -480
    },
    "id": "55f3df04-b7ee-4ad8-b76c-11f497287156"
   },
   "outputs": [],
   "source": [
    "df_trait = pd.read_csv('/content/gdrive/MyDrive/Lit/Lit_Submission/data/test_data.csv') \n",
    "df_BD = pd.read_csv('/content/gdrive/MyDrive/Lit/Lit_Submission/data/peptide-mhc-binding-affinity.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e315b4a8-6e8d-418f-ab67-f7924625b594",
   "metadata": {
    "id": "e315b4a8-6e8d-418f-ab67-f7924625b594"
   },
   "source": [
    "![图片.png](attachment:445cea85-dbe8-4532-992f-60415af19a2f.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9fb4fc2a-72d0-49cc-8331-fcddcc4a9712",
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1652117847966,
     "user": {
      "displayName": "Jayden Baxter",
      "userId": "11604221609513949563"
     },
     "user_tz": -480
    },
    "id": "9fb4fc2a-72d0-49cc-8331-fcddcc4a9712"
   },
   "outputs": [],
   "source": [
    "df_BD = df_BD.loc[:,['allele','peptide','measurement_value']]\n",
    "df_M = df_BD.loc[:,['measurement_value']]\n",
    "L = df_M['measurement_value'].values.tolist() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "201a2561-f9c4-4833-856e-f6e39392b1af",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 340,
     "status": "ok",
     "timestamp": 1652117849774,
     "user": {
      "displayName": "Jayden Baxter",
      "userId": "11604221609513949563"
     },
     "user_tz": -480
    },
    "id": "201a2561-f9c4-4833-856e-f6e39392b1af",
    "outputId": "95f1ab87-0f7d-476b-dbe8-7deb8e561554"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "223234"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "# math.log(0.0) 是一个不合法的值需要注意\n",
    "# 1-log(min(MS,50000))/log(50000)\n",
    "Lis = []\n",
    "for MS in L:\n",
    "    if MS == 0.0:\n",
    "        Lis.append(MS)\n",
    "    else:\n",
    "        value = 1-math.log(min(MS,50000))/math.log(50000)\n",
    "        Lis.append(value)\n",
    "len(Lis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bb55840f-fe31-4778-8bb7-cb3af8f9c382",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1652117851254,
     "user": {
      "displayName": "Jayden Baxter",
      "userId": "11604221609513949563"
     },
     "user_tz": -480
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    "id": "bb55840f-fe31-4778-8bb7-cb3af8f9c382",
    "outputId": "9a981e36-9557-404c-d8d6-36e99de635a3"
   },
   "outputs": [
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>BoLA-1*21:01</td>\n",
       "      <td>AENDTLVVSV</td>\n",
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       "      <th>1</th>\n",
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       "      <td>0.353937</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>AAHCIHAEW</td>\n",
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       "      <th>3</th>\n",
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       "      <th>4</th>\n",
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       "      <th>223229</th>\n",
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       "      <th>223230</th>\n",
       "      <td>SLA-3*02:02</td>\n",
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       "    <tr>\n",
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       "      <td>SLA-3*02:02</td>\n",
       "      <td>WLAFLSFSY</td>\n",
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       "    <tr>\n",
       "      <th>223232</th>\n",
       "      <td>SLA-3*02:02</td>\n",
       "      <td>WMMAMRYPI</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>223233</th>\n",
       "      <td>SLA-3*02:02</td>\n",
       "      <td>YQRTRALV</td>\n",
       "      <td>0.361562</td>\n",
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      "text/plain": [
       "              allele     peptide  measurement_value\n",
       "0       BoLA-1*21:01  AENDTLVVSV           0.171512\n",
       "1       BoLA-1*21:01  NQFNGGCLLV           0.353937\n",
       "2       BoLA-2*08:01   AAHCIHAEW           0.718615\n",
       "3       BoLA-2*08:01   AAKHMSNTY           0.337385\n",
       "4       BoLA-2*08:01  DSYAYMRNGW           0.935937\n",
       "...              ...         ...                ...\n",
       "223229   SLA-3*02:02   RRNYFTAEV           0.574375\n",
       "223230   SLA-3*02:02   SRLLSLWCI           0.212813\n",
       "223231   SLA-3*02:02   WLAFLSFSY           0.212813\n",
       "223232   SLA-3*02:02   WMMAMRYPI           0.425625\n",
       "223233   SLA-3*02:02    YQRTRALV           0.361562\n",
       "\n",
       "[223234 rows x 3 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_ = pd.DataFrame(np.array(Lis),columns=['measurement_value'])\n",
    "df_BD = pd.concat([df_BD.loc[:,['allele','peptide']],s_] , axis=1)\n",
    "df_BD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f9ba37ce-b4fa-4de1-bc37-a6e9153f2074",
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    "outputId": "68a70fb0-0051-4dde-e85b-58e8deeb4c42"
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    {
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       "      <td>WLAFLSFSY</td>\n",
       "      <td>0.212813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223232</th>\n",
       "      <td>SLA-3*02:02</td>\n",
       "      <td>WMMAMRYPI</td>\n",
       "      <td>0.425625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223233</th>\n",
       "      <td>SLA-3*02:02</td>\n",
       "      <td>YQRTRALV</td>\n",
       "      <td>0.361562</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>222875 rows × 3 columns</p>\n",
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       "  "
      ],
      "text/plain": [
       "              allele      peptide  measurement_value\n",
       "16      BoLA-2*12:01   AAHGMGKVGK           0.425625\n",
       "17      BoLA-2*12:01    ASFNYGAIK           0.611388\n",
       "18      BoLA-2*12:01   ASHGMGKVGK           0.425625\n",
       "19      BoLA-2*12:01  ASSHGMGKVGK           0.425625\n",
       "20      BoLA-2*12:01    CIYQITHGK           0.358383\n",
       "...              ...          ...                ...\n",
       "223229   SLA-3*02:02    RRNYFTAEV           0.574375\n",
       "223230   SLA-3*02:02    SRLLSLWCI           0.212813\n",
       "223231   SLA-3*02:02    WLAFLSFSY           0.212813\n",
       "223232   SLA-3*02:02    WMMAMRYPI           0.425625\n",
       "223233   SLA-3*02:02     YQRTRALV           0.361562\n",
       "\n",
       "[222875 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df_BD，因为我们只选择了那些肽数大于 20 的等位基因\n",
    "allocate = df_BD.loc[:,['allele']]             # 索引的是列\n",
    "lis = allocate['allele'].values.tolist()    # 将dataframe转换成列表\n",
    "\n",
    "ax = []\n",
    "res = []\n",
    "icount = 0\n",
    "for val in lis:\n",
    "#     line = val.split('*')[0]\n",
    "        if val in ax:\n",
    "            pass\n",
    "        else:\n",
    "            ax.append(val)\n",
    "\n",
    "res = []\n",
    "for val in ax:\n",
    "    if lis.count(val)>20:\n",
    "        res.append(val)\n",
    "\n",
    "df_ms = pd.DataFrame()\n",
    "for i in res:\n",
    "    df_1 = df_BD.loc[df_BD['allele'] == i]\n",
    "    df_ms = df_ms.append(df_1)\n",
    "df_ms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9LuhuLALi_mx",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 384,
     "status": "ok",
     "timestamp": 1652117871715,
     "user": {
      "displayName": "Jayden Baxter",
      "userId": "11604221609513949563"
     },
     "user_tz": -480
    },
    "id": "9LuhuLALi_mx",
    "outputId": "331bcc09-75b5-4ef1-c07c-2fb43c6fe7e7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ITDGDGSEHQQPQKTDEHKENQAKENEKKIQ\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "31"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 把氨基酸序列弄成等长，9mer [主要偏好，模式长度]\n",
    "List =df_ms['peptide'].values.tolist()\n",
    "# 确认最长的氨基酸序列\n",
    "len_list=map(len,List)\n",
    "li = list(len_list)  # 将映射实例化\n",
    "max_index=np.argmax(li)\n",
    "print(List[max_index])\n",
    "max(len(i) for i in List)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ln3kgl48jNPZ",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 359,
     "status": "ok",
     "timestamp": 1652117874221,
     "user": {
      "displayName": "Jayden Baxter",
      "userId": "11604221609513949563"
     },
     "user_tz": -480
    },
    "id": "ln3kgl48jNPZ",
    "outputId": "a8813e14-2739-4c69-a68c-4fe95b5f9ef9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "222875"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 因为氨基酸的锚点，14679 这5个位点比较重要需要尽可能保留，所以我们直接把超过9的部分删掉\n",
    "# 我们知道刚好等于9mer的有 222875-61637=161238 的确是优势地位占主导\n",
    "# 当然这个位置也可以是15等，因为最长的有31\n",
    "peptides = []\n",
    "for i in List:\n",
    "    if len(i) < 9:\n",
    "        num_1 = 9 - len(i)\n",
    "        al_1 = i + num_1 * 'X'\n",
    "        peptides.append(al_1)\n",
    "    if len(i) > 9:\n",
    "        # num_2 = len(i) - 9\n",
    "        al_2 = i[:9]\n",
    "        peptides.append(al_2)\n",
    "    if len(i) == 9:\n",
    "        peptides.append(i)        \n",
    "len(peptides)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "CLe9puK-jgzZ",
   "metadata": {
    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1652117877226,
     "user": {
      "displayName": "Jayden Baxter",
      "userId": "11604221609513949563"
     },
     "user_tz": -480
    },
    "id": "CLe9puK-jgzZ"
   },
   "outputs": [],
   "source": [
    "# 继续把氨基酸列给提出来\n",
    "allocate = df_ms.loc[:,['allele']]             # 索引的是列\n",
    "lis_2 = allocate['allele'].values.tolist()    # 将dataframe转换成列表\n",
    "\n",
    "# 把 test_data 的每一行提取出来，然后变成字典\n",
    "# L_1 = df_trait.iloc[0].to_list()\n",
    "# dic_1 = {L_1[0]:L_1[1:]}\n",
    "# L_2 = df_trait.iloc[1].to_list()\n",
    "# dic_2 = {L_2[0]:L_2[1:]}\n",
    "\n",
    "L_1 = df_trait.iloc[0].to_list()\n",
    "L_1 = L_1[1:]\n",
    "L_2 = df_trait.iloc[1].to_list()\n",
    "L_2 = L_2[1:]\n",
    "L_3 = df_trait.iloc[2].to_list()\n",
    "L_3 = L_3[1:]\n",
    "L_4 = df_trait.iloc[3].to_list()\n",
    "L_4 = L_4[1:]\n",
    "L_5 = df_trait.iloc[4].to_list()\n",
    "L_5 = L_5[1:]\n",
    "L_6 = df_trait.iloc[5].to_list()\n",
    "L_6 = L_6[1:]\n",
    "L_7 = df_trait.iloc[6].to_list()\n",
    "L_7 = L_7[1:]\n",
    "L_8 = df_trait.iloc[7].to_list()\n",
    "L_8 = L_8[1:]\n",
    "L_9 = df_trait.iloc[8].to_list()\n",
    "L_9 = L_9[1:]\n",
    "L_10 = df_trait.iloc[9].to_list()\n",
    "L_10 = L_10[1:]\n",
    "L_11 = df_trait.iloc[10].to_list()\n",
    "L_11 = L_11[1:]\n",
    "L_12 = df_trait.iloc[11].to_list()\n",
    "L_12 = L_12[1:]\n",
    "L_13 = df_trait.iloc[12].to_list()\n",
    "L_13 = L_13[1:]\n",
    "L_14 = df_trait.iloc[13].to_list()\n",
    "L_14 = L_14[1:]\n",
    "L_15 = df_trait.iloc[14].to_list()\n",
    "L_15 = L_15[1:]\n",
    "L_16 = df_trait.iloc[15].to_list()\n",
    "L_16 = L_16[1:]\n",
    "L_17 = df_trait.iloc[16].to_list()\n",
    "L_17 = L_17[1:]\n",
    "L_18 = df_trait.iloc[17].to_list()\n",
    "L_18 = L_18[1:]\n",
    "L_19 = df_trait.iloc[18].to_list()\n",
    "L_19 = L_19[1:]\n",
    "L_20 = df_trait.iloc[19].to_list()\n",
    "L_20 = L_20[1:]\n",
    "list_value = [L_1,L_2,L_3,L_4,L_5,L_6,L_7,L_8,L_9,L_10,L_11,L_12,L_13,L_14,L_15,L_16,L_17,L_18,L_19,L_20]\n",
    "\n",
    "# df = df_trait.loc[:,['name']]\n",
    "# val = np.array(df)\n",
    "# list_name = val.tolist() \n",
    "# list_name  (列表中的每个元素仍然是列表)\n",
    "\n",
    "df_name = df_trait['name'].values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "WbZ-uyamkYzT",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 363,
     "status": "ok",
     "timestamp": 1652117880076,
     "user": {
      "displayName": "Jayden Baxter",
      "userId": "11604221609513949563"
     },
     "user_tz": -480
    },
    "id": "WbZ-uyamkYzT",
    "outputId": "dbf86296-90bf-4dd6-b4cd-dd36aac46d64"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dic = {}\n",
    "for i in range(20):\n",
    "    dic[df_name[i]] = list_value[i]\n",
    "dic['X'] = [0]*25\n",
    "len(dic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "MeKRw0qPj4Ss",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 205
    },
    "executionInfo": {
     "elapsed": 613,
     "status": "ok",
     "timestamp": 1644587678962,
     "user": {
      "displayName": "Jayden Baxter",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "11604221609513949563"
     },
     "user_tz": -480
    },
    "id": "MeKRw0qPj4Ss",
    "outputId": "d2ee842b-ceb4-41d6-a53e-dbba93766047"
   },
   "outputs": [
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       "      <td>89.09</td>\n",
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       "      <td>1.0</td>\n",
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       "\n",
       "[2 rows x 225 columns]"
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     },
     "execution_count": 12,
     "metadata": {},
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   ],
   "source": [
    "# 增量编写\n",
    "# peptides:peptide list数据  ;   dic:feature dict数据  \n",
    "\n",
    "allodate = pd.DataFrame()\n",
    "# for line in List:\n",
    "#     for val in line:\n",
    "#         if val in dic:\n",
    "#             allodate.append(dic[val])\n",
    "\n",
    "# 先从一点开始往后叠加\n",
    "# pd.DataFrame(dic['V'])\n",
    "lis = peptides[0:2]\n",
    "for line in lis:\n",
    "    Lis = []\n",
    "    for val in line:\n",
    "      if val in dic:\n",
    "        Lis += (dic[val])\n",
    "    ipos = pd.DataFrame(Lis)\n",
    "    df_2 = ipos.stack()  \n",
    "    df_3 = df_2.unstack(0) # 将第二行的列索引转化成行索引\n",
    "    allodate = allodate.append(df_3)\n",
    "allodate"
   ]
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       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>32</th>\n",
       "      <th>33</th>\n",
       "      <th>34</th>\n",
       "      <th>35</th>\n",
       "      <th>36</th>\n",
       "      <th>37</th>\n",
       "      <th>38</th>\n",
       "      <th>39</th>\n",
       "      <th>...</th>\n",
       "      <th>185</th>\n",
       "      <th>186</th>\n",
       "      <th>187</th>\n",
       "      <th>188</th>\n",
       "      <th>189</th>\n",
       "      <th>190</th>\n",
       "      <th>191</th>\n",
       "      <th>192</th>\n",
       "      <th>193</th>\n",
       "      <th>194</th>\n",
       "      <th>195</th>\n",
       "      <th>196</th>\n",
       "      <th>197</th>\n",
       "      <th>198</th>\n",
       "      <th>199</th>\n",
       "      <th>200</th>\n",
       "      <th>201</th>\n",
       "      <th>202</th>\n",
       "      <th>203</th>\n",
       "      <th>204</th>\n",
       "      <th>205</th>\n",
       "      <th>206</th>\n",
       "      <th>207</th>\n",
       "      <th>208</th>\n",
       "      <th>209</th>\n",
       "      <th>210</th>\n",
       "      <th>211</th>\n",
       "      <th>212</th>\n",
       "      <th>213</th>\n",
       "      <th>214</th>\n",
       "      <th>215</th>\n",
       "      <th>216</th>\n",
       "      <th>217</th>\n",
       "      <th>218</th>\n",
       "      <th>219</th>\n",
       "      <th>220</th>\n",
       "      <th>221</th>\n",
       "      <th>222</th>\n",
       "      <th>223</th>\n",
       "      <th>224</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5950.0</td>\n",
       "      <td>89.09</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>63.3</td>\n",
       "      <td>61.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>68.8</td>\n",
       "      <td>1.92</td>\n",
       "      <td>1.35</td>\n",
       "      <td>0.68</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5950.0</td>\n",
       "      <td>89.09</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>63.3</td>\n",
       "      <td>61.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>68.8</td>\n",
       "      <td>1.92</td>\n",
       "      <td>1.35</td>\n",
       "      <td>0.68</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>94.4</td>\n",
       "      <td>2.37</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.03</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>75.07</td>\n",
       "      <td>-3.2</td>\n",
       "      <td>63.3</td>\n",
       "      <td>42.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.2</td>\n",
       "      <td>1.90</td>\n",
       "      <td>1.06</td>\n",
       "      <td>0.56</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5950.0</td>\n",
       "      <td>89.09</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>63.3</td>\n",
       "      <td>61.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>68.8</td>\n",
       "      <td>1.92</td>\n",
       "      <td>1.35</td>\n",
       "      <td>0.68</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5951.0</td>\n",
       "      <td>105.09</td>\n",
       "      <td>-3.1</td>\n",
       "      <td>83.6</td>\n",
       "      <td>72.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>76.6</td>\n",
       "      <td>1.98</td>\n",
       "      <td>1.53</td>\n",
       "      <td>0.79</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>106.1</td>\n",
       "      <td>3.29</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.05</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5962.0</td>\n",
       "      <td>146.19</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>89.3</td>\n",
       "      <td>106.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>116.7</td>\n",
       "      <td>7.75</td>\n",
       "      <td>1.15</td>\n",
       "      <td>0.68</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5950.0</td>\n",
       "      <td>89.09</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>63.3</td>\n",
       "      <td>61.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>68.8</td>\n",
       "      <td>1.92</td>\n",
       "      <td>1.35</td>\n",
       "      <td>0.68</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5951.0</td>\n",
       "      <td>105.09</td>\n",
       "      <td>-3.1</td>\n",
       "      <td>83.6</td>\n",
       "      <td>72.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>76.6</td>\n",
       "      <td>1.98</td>\n",
       "      <td>1.53</td>\n",
       "      <td>0.79</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>94.4</td>\n",
       "      <td>2.37</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.03</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>75.07</td>\n",
       "      <td>-3.2</td>\n",
       "      <td>63.3</td>\n",
       "      <td>42.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.2</td>\n",
       "      <td>1.90</td>\n",
       "      <td>1.06</td>\n",
       "      <td>0.56</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5950.0</td>\n",
       "      <td>89.09</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>63.3</td>\n",
       "      <td>61.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>68.8</td>\n",
       "      <td>1.92</td>\n",
       "      <td>1.35</td>\n",
       "      <td>0.68</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5951.0</td>\n",
       "      <td>105.09</td>\n",
       "      <td>-3.1</td>\n",
       "      <td>83.6</td>\n",
       "      <td>72.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>76.6</td>\n",
       "      <td>1.98</td>\n",
       "      <td>1.53</td>\n",
       "      <td>0.79</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>116.7</td>\n",
       "      <td>7.75</td>\n",
       "      <td>1.15</td>\n",
       "      <td>0.68</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6287.0</td>\n",
       "      <td>117.15</td>\n",
       "      <td>-2.3</td>\n",
       "      <td>63.3</td>\n",
       "      <td>90.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>94.4</td>\n",
       "      <td>2.37</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.03</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5862.0</td>\n",
       "      <td>121.16</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>64.3</td>\n",
       "      <td>75.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>87.4</td>\n",
       "      <td>2.36</td>\n",
       "      <td>1.58</td>\n",
       "      <td>0.90</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6306.0</td>\n",
       "      <td>131.17</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>63.3</td>\n",
       "      <td>103.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>106.1</td>\n",
       "      <td>3.29</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.05</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.2</td>\n",
       "      <td>1.90</td>\n",
       "      <td>1.06</td>\n",
       "      <td>0.56</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5962.0</td>\n",
       "      <td>146.19</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>89.3</td>\n",
       "      <td>106.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>116.7</td>\n",
       "      <td>7.75</td>\n",
       "      <td>1.15</td>\n",
       "      <td>0.68</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33032.0</td>\n",
       "      <td>147.13</td>\n",
       "      <td>-3.7</td>\n",
       "      <td>101.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>108.5</td>\n",
       "      <td>4.65</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1.04</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6288.0</td>\n",
       "      <td>119.12</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>83.6</td>\n",
       "      <td>93.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>88.9</td>\n",
       "      <td>2.20</td>\n",
       "      <td>1.48</td>\n",
       "      <td>0.98</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>140.1</td>\n",
       "      <td>6.02</td>\n",
       "      <td>1.60</td>\n",
       "      <td>0.87</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5951.0</td>\n",
       "      <td>105.09</td>\n",
       "      <td>-3.1</td>\n",
       "      <td>83.6</td>\n",
       "      <td>72.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>76.6</td>\n",
       "      <td>1.98</td>\n",
       "      <td>1.53</td>\n",
       "      <td>0.79</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33032.0</td>\n",
       "      <td>147.13</td>\n",
       "      <td>-3.7</td>\n",
       "      <td>101.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>108.5</td>\n",
       "      <td>4.65</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1.04</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6288.0</td>\n",
       "      <td>119.12</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>83.6</td>\n",
       "      <td>93.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>88.9</td>\n",
       "      <td>2.20</td>\n",
       "      <td>1.48</td>\n",
       "      <td>0.98</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>2.73</td>\n",
       "      <td>1.59</td>\n",
       "      <td>1.07</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6287.0</td>\n",
       "      <td>117.15</td>\n",
       "      <td>-2.3</td>\n",
       "      <td>63.3</td>\n",
       "      <td>90.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>94.4</td>\n",
       "      <td>2.37</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.03</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33032.0</td>\n",
       "      <td>147.13</td>\n",
       "      <td>-3.7</td>\n",
       "      <td>101.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>108.5</td>\n",
       "      <td>4.65</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1.04</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
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       "      <td>6288.0</td>\n",
       "      <td>119.12</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>83.6</td>\n",
       "      <td>93.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>88.9</td>\n",
       "      <td>2.20</td>\n",
       "      <td>1.48</td>\n",
       "      <td>0.98</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>130.7</td>\n",
       "      <td>7.45</td>\n",
       "      <td>1.49</td>\n",
       "      <td>0.86</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6322.0</td>\n",
       "      <td>174.20</td>\n",
       "      <td>-4.2</td>\n",
       "      <td>128.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>130.7</td>\n",
       "      <td>7.45</td>\n",
       "      <td>1.49</td>\n",
       "      <td>0.86</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33032.0</td>\n",
       "      <td>147.13</td>\n",
       "      <td>-3.7</td>\n",
       "      <td>101.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>108.5</td>\n",
       "      <td>4.65</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1.04</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6288.0</td>\n",
       "      <td>119.12</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>83.6</td>\n",
       "      <td>93.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>88.9</td>\n",
       "      <td>2.20</td>\n",
       "      <td>1.48</td>\n",
       "      <td>0.98</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>76.6</td>\n",
       "      <td>1.98</td>\n",
       "      <td>1.53</td>\n",
       "      <td>0.79</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6057.0</td>\n",
       "      <td>181.19</td>\n",
       "      <td>-2.3</td>\n",
       "      <td>83.6</td>\n",
       "      <td>176.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>140.1</td>\n",
       "      <td>6.02</td>\n",
       "      <td>1.60</td>\n",
       "      <td>0.87</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33032.0</td>\n",
       "      <td>147.13</td>\n",
       "      <td>-3.7</td>\n",
       "      <td>101.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>108.5</td>\n",
       "      <td>4.65</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1.04</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6288.0</td>\n",
       "      <td>119.12</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>83.6</td>\n",
       "      <td>93.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>88.9</td>\n",
       "      <td>2.20</td>\n",
       "      <td>1.48</td>\n",
       "      <td>0.98</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>140.1</td>\n",
       "      <td>6.02</td>\n",
       "      <td>1.60</td>\n",
       "      <td>0.87</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5950.0</td>\n",
       "      <td>89.09</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>63.3</td>\n",
       "      <td>61.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>68.8</td>\n",
       "      <td>1.92</td>\n",
       "      <td>1.35</td>\n",
       "      <td>0.68</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
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       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-fd36c575-a280-4da5-b8b9-8f3b9efb5726 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
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       "          const element = document.querySelector('#df-fd36c575-a280-4da5-b8b9-8f3b9efb5726');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
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       "  "
      ],
      "text/plain": [
       "        0       1    2      3      4    5    ...  219  220  221  222  223   224\n",
       "0    5950.0   89.09 -3.0   63.3   61.8  2.0  ...  1.0  0.0  0.0  0.4  1.0   2.0\n",
       "0    5950.0   89.09 -3.0   63.3   61.8  2.0  ...  2.0  0.0  0.0  0.6  5.0  10.0\n",
       "0    5950.0   89.09 -3.0   63.3   61.8  2.0  ...  1.0  0.0  0.0  0.4  1.0   2.0\n",
       "0    5950.0   89.09 -3.0   63.3   61.8  2.0  ...  1.0  0.0  1.0  0.4  2.0   8.0\n",
       "0    5862.0  121.16 -2.5   64.3   75.3  3.0  ...  2.0  0.0  0.0  0.6  5.0  10.0\n",
       "..      ...     ...  ...    ...    ...  ...  ...  ...  ...  ...  ...  ...   ...\n",
       "0   33032.0  147.13 -3.7  101.0  145.0  3.0  ...  1.0  0.0  0.0  0.4  2.0   9.0\n",
       "0   33032.0  147.13 -3.7  101.0  145.0  3.0  ...  1.0  0.0  1.0  0.4  2.0   8.0\n",
       "0   33032.0  147.13 -3.7  101.0  145.0  3.0  ...  2.0  0.0  0.0  0.6  5.0  10.0\n",
       "0   33032.0  147.13 -3.7  101.0  145.0  3.0  ...  1.0  1.0  0.0  0.6  3.0  10.0\n",
       "0   33032.0  147.13 -3.7  101.0  145.0  3.0  ...  1.0  0.0  0.0  0.4  1.0   3.0\n",
       "\n",
       "[50000 rows x 225 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 由于数据量较大，对全部来进行操作可能有点困难，计算机的运算可能有点不太够\n",
    "# 因此我们考虑分成四等份\n",
    "len(peptides)   # 长度为 222875\n",
    "peptides_1 = peptides[:50000]\n",
    "peptides_2 = peptides[50000:100000]\n",
    "peptides_3 = peptides[100000:150000]\n",
    "peptides_4 = peptides[150000:200000]\n",
    "peptides_5 = peptides[200000:]\n",
    "\n",
    "allodate = pd.DataFrame()\n",
    "for line in peptides_1:\n",
    "  Lis = []\n",
    "  for val in line:\n",
    "    if val in dic:\n",
    "      Lis += (dic[val])\n",
    "  ipos = pd.DataFrame(Lis)\n",
    "  df_3 = ipos.T \n",
    "  allodate = allodate.append(df_3)\n",
    "allodate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0GYGkXRxJkeE",
   "metadata": {
    "id": "0GYGkXRxJkeE"
   },
   "outputs": [],
   "source": [
    "peptides_2 = peptides[50000:100000]\n",
    "peptides_3 = peptides[100000:150000]\n",
    "peptides_4 = peptides[150000:200000]\n",
    "peptides_5 = peptides[200000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "yRYF6vGXsNqy",
   "metadata": {
    "id": "yRYF6vGXsNqy"
   },
   "outputs": [],
   "source": [
    "# 对全部来进行这样的操作\n",
    "allodate_1 = pd.DataFrame()\n",
    "for line in peptides_2:\n",
    "  Lis = []\n",
    "  for val in line:\n",
    "    if val in dic:\n",
    "      Lis += (dic[val])\n",
    "  ipos = pd.DataFrame(Lis)\n",
    "  df_3 = ipos.T \n",
    "  allodate_1 = allodate_1.append(df_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "HfWHPa7ELIf1",
   "metadata": {
    "id": "HfWHPa7ELIf1"
   },
   "outputs": [],
   "source": [
    "allodate_2 = pd.DataFrame()\n",
    "for line in peptides_3:\n",
    "  Lis = []\n",
    "  for val in line:\n",
    "    if val in dic:\n",
    "      Lis += (dic[val])\n",
    "  ipos = pd.DataFrame(Lis)\n",
    "  df_3 = ipos.T \n",
    "  allodate_2 = allodate_2.append(df_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "RGVngxztLJ-P",
   "metadata": {
    "id": "RGVngxztLJ-P"
   },
   "outputs": [],
   "source": [
    "allodate_3 = pd.DataFrame()\n",
    "for line in peptides_4:\n",
    "  Lis = []\n",
    "  for val in line:\n",
    "    if val in dic:\n",
    "      Lis += (dic[val])\n",
    "  ipos = pd.DataFrame(Lis)\n",
    "  df_3 = ipos.T \n",
    "  allodate_3 = allodate_3.append(df_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "q1apAGpQLP-Q",
   "metadata": {
    "id": "q1apAGpQLP-Q"
   },
   "outputs": [],
   "source": [
    "allodate_4 = pd.DataFrame()\n",
    "for line in peptides_5:\n",
    "  Lis = []\n",
    "  for val in line:\n",
    "    if val in dic:\n",
    "      Lis += (dic[val])\n",
    "  ipos = pd.DataFrame(Lis)\n",
    "  df_3 = ipos.T \n",
    "  allodate_4 = allodate_4.append(df_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "gQj3MVe8TzMa",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 487
    },
    "executionInfo": {
     "elapsed": 1128,
     "status": "ok",
     "timestamp": 1644592274308,
     "user": {
      "displayName": "Jayden Baxter",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "11604221609513949563"
     },
     "user_tz": -480
    },
    "id": "gQj3MVe8TzMa",
    "outputId": "688549ed-660d-4ca0-8b4b-776870cd8b64"
   },
   "outputs": [
    {
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       "      <td>750.0</td>\n",
       "      <td>75.07</td>\n",
       "      <td>-3.2</td>\n",
       "      <td>63.3</td>\n",
       "      <td>42.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.2</td>\n",
       "      <td>1.90</td>\n",
       "      <td>1.06</td>\n",
       "      <td>0.56</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5950.0</td>\n",
       "      <td>89.09</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>63.3</td>\n",
       "      <td>61.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>68.8</td>\n",
       "      <td>1.92</td>\n",
       "      <td>1.35</td>\n",
       "      <td>0.68</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5951.0</td>\n",
       "      <td>105.09</td>\n",
       "      <td>-3.1</td>\n",
       "      <td>83.6</td>\n",
       "      <td>72.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>76.6</td>\n",
       "      <td>1.98</td>\n",
       "      <td>1.53</td>\n",
       "      <td>0.79</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>116.7</td>\n",
       "      <td>7.75</td>\n",
       "      <td>1.15</td>\n",
       "      <td>0.68</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6287.0</td>\n",
       "      <td>117.15</td>\n",
       "      <td>-2.3</td>\n",
       "      <td>63.3</td>\n",
       "      <td>90.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>94.4</td>\n",
       "      <td>2.37</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.03</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5862.0</td>\n",
       "      <td>121.16</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>64.3</td>\n",
       "      <td>75.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>87.4</td>\n",
       "      <td>2.36</td>\n",
       "      <td>1.58</td>\n",
       "      <td>0.90</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6306.0</td>\n",
       "      <td>131.17</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>63.3</td>\n",
       "      <td>103.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>106.1</td>\n",
       "      <td>3.29</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.05</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.2</td>\n",
       "      <td>1.90</td>\n",
       "      <td>1.06</td>\n",
       "      <td>0.56</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5962.0</td>\n",
       "      <td>146.19</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>89.3</td>\n",
       "      <td>106.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>116.7</td>\n",
       "      <td>7.75</td>\n",
       "      <td>1.15</td>\n",
       "      <td>0.68</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6322.0</td>\n",
       "      <td>174.20</td>\n",
       "      <td>-4.2</td>\n",
       "      <td>128.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>130.7</td>\n",
       "      <td>7.45</td>\n",
       "      <td>1.49</td>\n",
       "      <td>0.86</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6322.0</td>\n",
       "      <td>174.20</td>\n",
       "      <td>-4.2</td>\n",
       "      <td>128.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>130.7</td>\n",
       "      <td>7.45</td>\n",
       "      <td>1.49</td>\n",
       "      <td>0.86</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>108.5</td>\n",
       "      <td>4.65</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1.04</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6287.0</td>\n",
       "      <td>117.15</td>\n",
       "      <td>-2.3</td>\n",
       "      <td>63.3</td>\n",
       "      <td>90.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>94.4</td>\n",
       "      <td>2.37</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.03</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5951.0</td>\n",
       "      <td>105.09</td>\n",
       "      <td>-3.1</td>\n",
       "      <td>83.6</td>\n",
       "      <td>72.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>76.6</td>\n",
       "      <td>1.98</td>\n",
       "      <td>1.53</td>\n",
       "      <td>0.79</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6322.0</td>\n",
       "      <td>174.20</td>\n",
       "      <td>-4.2</td>\n",
       "      <td>128.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>130.7</td>\n",
       "      <td>7.45</td>\n",
       "      <td>1.49</td>\n",
       "      <td>0.86</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>87.4</td>\n",
       "      <td>2.36</td>\n",
       "      <td>1.58</td>\n",
       "      <td>0.90</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6306.0</td>\n",
       "      <td>131.17</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>63.3</td>\n",
       "      <td>103.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>106.1</td>\n",
       "      <td>3.29</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.05</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6305.0</td>\n",
       "      <td>204.22</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>79.1</td>\n",
       "      <td>245.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>158.4</td>\n",
       "      <td>5.19</td>\n",
       "      <td>2.17</td>\n",
       "      <td>0.99</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6106.0</td>\n",
       "      <td>131.17</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>63.3</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>2.73</td>\n",
       "      <td>1.59</td>\n",
       "      <td>1.07</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>76.6</td>\n",
       "      <td>1.98</td>\n",
       "      <td>1.53</td>\n",
       "      <td>0.79</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6057.0</td>\n",
       "      <td>181.19</td>\n",
       "      <td>-2.3</td>\n",
       "      <td>83.6</td>\n",
       "      <td>176.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>140.1</td>\n",
       "      <td>6.02</td>\n",
       "      <td>1.60</td>\n",
       "      <td>0.87</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "\n",
       "[222875 rows x 225 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = pd.concat([allodate,allodate_1,allodate_2,allodate_3,allodate_4])\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23R9y-OJVsl1",
   "metadata": {
    "id": "23R9y-OJVsl1"
   },
   "outputs": [],
   "source": [
    "a.to_csv('trait.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7ejlK6vOWFAs",
   "metadata": {
    "executionInfo": {
     "elapsed": 6573,
     "status": "ok",
     "timestamp": 1652117898249,
     "user": {
      "displayName": "Jayden Baxter",
      "userId": "11604221609513949563"
     },
     "user_tz": -480
    },
    "id": "7ejlK6vOWFAs"
   },
   "outputs": [],
   "source": [
    "df_features = pd.read_csv('/content/gdrive/MyDrive/Lit/Lit_Submission/data/trait.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "BmD9dpaB7KfT",
   "metadata": {
    "colab": {
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     "height": 467
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    "executionInfo": {
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     "status": "ok",
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     "user": {
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    "id": "BmD9dpaB7KfT",
    "outputId": "64fee6c1-d7e7-45e4-80a6-d9f3db78f92e"
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   "outputs": [
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222870</th>\n",
       "      <td>6322</td>\n",
       "      <td>174.20</td>\n",
       "      <td>-4.2</td>\n",
       "      <td>128.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222871</th>\n",
       "      <td>5951</td>\n",
       "      <td>105.09</td>\n",
       "      <td>-3.1</td>\n",
       "      <td>83.6</td>\n",
       "      <td>72.6</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222872</th>\n",
       "      <td>6305</td>\n",
       "      <td>204.22</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>79.1</td>\n",
       "      <td>245.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222873</th>\n",
       "      <td>6305</td>\n",
       "      <td>204.22</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>79.1</td>\n",
       "      <td>245.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222874</th>\n",
       "      <td>6057</td>\n",
       "      <td>181.19</td>\n",
       "      <td>-2.3</td>\n",
       "      <td>83.6</td>\n",
       "      <td>176.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>222875 rows × 225 columns</p>\n",
       "</div>\n",
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       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
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       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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      ],
      "text/plain": [
       "           0       1    2      3      4  5  6  7   8  9  ...  215  216  217  \\\n",
       "0       5950   89.09 -3.0   63.3   61.8  2  3  1   6  1  ...    5    2    1   \n",
       "1       5950   89.09 -3.0   63.3   61.8  2  3  1   6  1  ...    7    2    2   \n",
       "2       5950   89.09 -3.0   63.3   61.8  2  3  1   6  1  ...    5    2    1   \n",
       "3       5950   89.09 -3.0   63.3   61.8  2  3  1   6  1  ...    6    2    1   \n",
       "4       5862  121.16 -2.5   64.3   75.3  3  4  2   7  1  ...    7    2    2   \n",
       "...      ...     ...  ...    ...    ... .. .. ..  .. ..  ...  ...  ...  ...   \n",
       "222870  6322  174.20 -4.2  128.0  176.0  4  4  5  12  1  ...    6    2    1   \n",
       "222871  5951  105.09 -3.1   83.6   72.6  3  4  2   7  1  ...    7    2    1   \n",
       "222872  6305  204.22 -1.1   79.1  245.0  3  3  3  15  1  ...    7    2    2   \n",
       "222873  6305  204.22 -1.1   79.1  245.0  3  3  3  15  1  ...    7    2    1   \n",
       "222874  6057  181.19 -2.3   83.6  176.0  3  4  3  13  1  ...    0    0    0   \n",
       "\n",
       "        218  219  220  221  222  223  224  \n",
       "0         1    1    0    0  0.4    1    2  \n",
       "1         1    2    0    0  0.6    5   10  \n",
       "2         1    1    0    0  0.4    1    2  \n",
       "3         1    1    0    1  0.4    2    8  \n",
       "4         1    2    0    0  0.6    5   10  \n",
       "...     ...  ...  ...  ...  ...  ...  ...  \n",
       "222870    1    1    0    1  0.4    2    8  \n",
       "222871    1    1    0    2  0.6    3   10  \n",
       "222872    1    1    1    0  0.6    3   10  \n",
       "222873    1    1    0    2  0.6    3   10  \n",
       "222874    0    0    0    0  0.0    0    0  \n",
       "\n",
       "[222875 rows x 225 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_features"
   ]
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 ],
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